about this book
This book is for
- Machine learning engineers looking to incorporate causality into AI systems and build more robust predictive models
- Data scientists who are looking to expand both their causal inference and machine learning skillsets
- Researchers who want a wholistic view of causal inference and how it connects to their domain of expertise without going down stats theory rabbit holes
- AI product experts looking for case studies in business settings, especially tech and retail
- People who want to get in on the ground floor of causal AI
What is the required mathematical and programming background?
Rest assured, this book doesn’t require a deep background in probability and statistics theory. The relationship between causality and statistics is like the relationship between engineering and math. Engineering involves a lot of math, but you need only a bit of math to learn core engineering concepts. After learning those concepts and digging into an applied problem, you can focus on learning the extra math you need to go deep on that problem.
This book assumes a level of familiarity with probability and statistics typical of a data scientist. Specifically, it assumes you have basic knowledge of